scholarly journals INVESTIGATING PERFORMANCE OF INTELLIGENT SINE COSINE ALGORITHM WITHIN UNCONSTRAINED UNIMODAL AND MULTIMODAL SEARCH AND OPTIMIZATION PROBLEMS

2017 ◽  
Vol 1 (1) ◽  
pp. 11-22
Author(s):  
- Harun BİNGÖL - Bilal ALATAS
Keyword(s):  
Optimization Problems ◽  
Sine Cosine Algorithm

A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

2021 ◽  
Vol 12 (1) ◽  
pp. 49-66
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Jingsen Liu
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Population Diversity ◽  
Lévy Flight ◽  
Simulation Experiments ◽  
Mathematical Functions ◽  
Levy Flight ◽  
Engineering Problems ◽  
Sine Cosine Algorithm ◽  
Swarm Intelligence Algorithm

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

Cloud model based sine cosine algorithm for solving optimization problems

2019 ◽  
Vol 12 (4) ◽  
pp. 503-514 ◽  
Author(s):  
Jiatang Cheng ◽  
Zhimei Duan
Keyword(s):  
Optimization Problems ◽  
Cloud Model ◽  
Model Based ◽  
Sine Cosine Algorithm

PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems

2018 ◽  
Vol 73 ◽  
pp. 697-726 ◽  
Author(s):  
Saeed Nezamivand Chegini ◽  
Ahmad Bagheri ◽  
Farid Najafi
Keyword(s):  
Optimization Problems ◽  
Lévy Flight ◽  
Levy Flight ◽  
Sine Cosine Algorithm

scholarly journals A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Chiwen Qu ◽  
Zhiliu Zeng ◽  
Jun Dai ◽  
Zhongjun Yi ◽  
Wei He
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Convergence Speed ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Search Range ◽  
Inertia Weight ◽  
Mutation Strategy ◽  
Sine Cosine Algorithm ◽  
Conversion Parameter

For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.

An Improved Sine Cosine Algorithm for Solving Optimization Problems

2018 ◽  
Author(s):  
M. H. Suid ◽  
M. A. Ahmad ◽  
M. R. T. R. Ismail ◽  
M. R. Ghazali ◽  
A. Irawan ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Sine Cosine Algorithm

scholarly journals A Hybrid SCA Inspired BBO for Feature Selection Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
R. Sindhu ◽  
Ruzelita Ngadiran ◽  
Yasmin Mohd Yacob ◽  
Nik Adilah Hanin Zahri ◽  
M. Hariharan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Problems ◽  
Feature Selection Method ◽  
Search Algorithms ◽  
The Other ◽  
Test Functions ◽  
Ranking Methods ◽  
Selection Problems ◽  
Benchmark Datasets ◽  
Sine Cosine Algorithm

Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.

scholarly journals The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hongping Hu ◽  
Yangyang Li ◽  
Yanping Bai ◽  
Juping Zhang ◽  
Maoxing Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Optimization Problems ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Sine Cosine Algorithm ◽  
Complex Optimization ◽  
Predicted Model ◽  
Moth Flame Optimization Algorithm ◽  
Development Capability

The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

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